Overview

Dataset statistics

Number of variables55
Number of observations58101
Missing cells0
Missing cells (%)0.0%
Duplicate rows2671
Duplicate rows (%)4.6%
Total size in memory24.0 MiB
Average record size in memory433.0 B

Variable types

Numeric10
Categorical44
Boolean1

Alerts

Dataset has 2671 (4.6%) duplicate rowsDuplicates
Aspect is highly correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area_0 is highly correlated with Wilderness_Area_2 and 1 other fieldsHigh correlation
Wilderness_Area_2 is highly correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly correlated with Cover_TypeHigh correlation
Soil_Type_28 is highly correlated with Wilderness_Area_0High correlation
Cover_Type is highly correlated with Wilderness_Area_3High correlation
Elevation is highly correlated with Wilderness_Area_3 and 1 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 1 other fieldsHigh correlation
Slope is highly correlated with Hillshade_NoonHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Aspect and 1 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Slope and 1 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area_0 is highly correlated with Wilderness_Area_2 and 1 other fieldsHigh correlation
Wilderness_Area_2 is highly correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type_28 is highly correlated with Wilderness_Area_0High correlation
Cover_Type is highly correlated with Elevation and 1 other fieldsHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Hillshade_9amHigh correlation
Wilderness_Area_0 is highly correlated with Wilderness_Area_2 and 1 other fieldsHigh correlation
Wilderness_Area_2 is highly correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly correlated with Cover_TypeHigh correlation
Soil_Type_28 is highly correlated with Wilderness_Area_0High correlation
Cover_Type is highly correlated with Wilderness_Area_3High correlation
Wilderness_Area_2 is highly correlated with Wilderness_Area_0High correlation
Cover_Type is highly correlated with Wilderness_Area_3High correlation
Wilderness_Area_0 is highly correlated with Wilderness_Area_2 and 1 other fieldsHigh correlation
Soil_Type_28 is highly correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly correlated with Cover_TypeHigh correlation
Elevation is highly correlated with Horizontal_Distance_To_Roadways and 4 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Slope is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with Elevation and 2 other fieldsHigh correlation
Hillshade_9am is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 3 other fieldsHigh correlation
Wilderness_Area_0 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
Wilderness_Area_2 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
Wilderness_Area_3 is highly correlated with Elevation and 3 other fieldsHigh correlation
Soil_Type_5 is highly correlated with Wilderness_Area_3High correlation
Soil_Type_9 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type_28 is highly correlated with Wilderness_Area_0 and 1 other fieldsHigh correlation
Soil_Type_39 is highly correlated with ElevationHigh correlation
Cover_Type is highly correlated with Elevation and 1 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology has 2426 (4.2%) zeros Zeros
Vertical_Distance_To_Hydrology has 3815 (6.6%) zeros Zeros

Reproduction

Analysis started2022-05-10 10:41:45.738104
Analysis finished2022-05-10 10:42:50.125273
Duration1 minute and 4.39 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1724
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2959.401749
Minimum1861
Maximum3850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:50.282123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile2401
Q12811
median2996
Q33165
95-th percentile3334
Maximum3850
Range1989
Interquartile range (IQR)354

Descriptive statistics

Standard deviation280.8905978
Coefficient of variation (CV)0.09491465561
Kurtosis0.7624412792
Mean2959.401749
Median Absolute Deviation (MAD)176
Skewness-0.8339606698
Sum171944201
Variance78899.52792
MonotonicityNot monotonic
2022-05-10T12:42:50.412997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968184
 
0.3%
2955179
 
0.3%
2972179
 
0.3%
2985173
 
0.3%
2978172
 
0.3%
2962172
 
0.3%
2991170
 
0.3%
2988168
 
0.3%
2965162
 
0.3%
2975160
 
0.3%
Other values (1714)56382
97.0%
ValueCountFrequency (%)
18611
< 0.1%
18682
< 0.1%
18711
< 0.1%
18722
< 0.1%
18761
< 0.1%
18821
< 0.1%
18851
< 0.1%
18861
< 0.1%
18901
< 0.1%
18911
< 0.1%
ValueCountFrequency (%)
38501
< 0.1%
38481
< 0.1%
38371
< 0.1%
38361
< 0.1%
38321
< 0.1%
38292
< 0.1%
38281
< 0.1%
38171
< 0.1%
38151
< 0.1%
38131
< 0.1%

Aspect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct361
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.058209
Minimum0
Maximum360
Zeros476
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:50.547761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3259
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)201

Descriptive statistics

Standard deviation111.712352
Coefficient of variation (CV)0.7204542909
Kurtosis-1.212579785
Mean155.058209
Median Absolute Deviation (MAD)85
Skewness0.4081036941
Sum9009037
Variance12479.64959
MonotonicityNot monotonic
2022-05-10T12:42:50.668649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45624
 
1.1%
0476
 
0.8%
90475
 
0.8%
135417
 
0.7%
315372
 
0.6%
18364
 
0.6%
63353
 
0.6%
27350
 
0.6%
108305
 
0.5%
22291
 
0.5%
Other values (351)54074
93.1%
ValueCountFrequency (%)
0476
0.8%
1149
 
0.3%
2196
0.3%
3194
0.3%
4265
0.5%
5201
0.3%
6201
0.3%
7221
0.4%
8192
0.3%
9257
0.4%
ValueCountFrequency (%)
3604
 
< 0.1%
359151
0.3%
358162
0.3%
357180
0.3%
356185
0.3%
355184
0.3%
354211
0.4%
353203
0.3%
352178
0.3%
351224
0.4%

Slope
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.11776045
Minimum0
Maximum65
Zeros61
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:50.796369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum65
Range65
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.47833622
Coefficient of variation (CV)0.5297112276
Kurtosis0.6404889389
Mean14.11776045
Median Absolute Deviation (MAD)5
Skewness0.7994538913
Sum820256
Variance55.92551262
MonotonicityNot monotonic
2022-05-10T12:42:50.912418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113422
 
5.9%
123336
 
5.7%
103305
 
5.7%
133293
 
5.7%
93207
 
5.5%
143036
 
5.2%
82894
 
5.0%
152877
 
5.0%
72673
 
4.6%
162651
 
4.6%
Other values (46)27407
47.2%
ValueCountFrequency (%)
061
 
0.1%
1336
 
0.6%
2707
 
1.2%
31182
 
2.0%
41710
2.9%
52105
3.6%
62427
4.2%
72673
4.6%
82894
5.0%
93207
5.5%
ValueCountFrequency (%)
651
 
< 0.1%
611
 
< 0.1%
531
 
< 0.1%
521
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
495
< 0.1%
484
< 0.1%
479
< 0.1%
469
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct456
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.41822
Minimum0
Maximum1338
Zeros2426
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:51.041679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3384
95-th percentile679
Maximum1338
Range1338
Interquartile range (IQR)276

Descriptive statistics

Standard deviation210.6912544
Coefficient of variation (CV)0.7849364862
Kurtosis1.301800083
Mean268.41822
Median Absolute Deviation (MAD)133
Skewness1.123997443
Sum15595367
Variance44390.80469
MonotonicityNot monotonic
2022-05-10T12:42:51.166530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303406
 
5.9%
02426
 
4.2%
1502089
 
3.6%
601869
 
3.2%
671526
 
2.6%
421481
 
2.5%
1081469
 
2.5%
851382
 
2.4%
901145
 
2.0%
1201026
 
1.8%
Other values (446)40282
69.3%
ValueCountFrequency (%)
02426
4.2%
303406
5.9%
421481
2.5%
601869
3.2%
671526
2.6%
851382
2.4%
901145
 
2.0%
95924
 
1.6%
1081469
2.5%
1201026
 
1.8%
ValueCountFrequency (%)
13381
< 0.1%
13191
< 0.1%
13021
< 0.1%
13001
< 0.1%
12911
< 0.1%
12901
< 0.1%
12762
< 0.1%
12751
< 0.1%
12731
< 0.1%
12631
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct544
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.07574741
Minimum-166
Maximum581
Zeros3815
Zeros (%)6.6%
Negative5592
Negative (%)9.6%
Memory size454.0 KiB
2022-05-10T12:42:51.298183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-166
5-th percentile-8
Q17
median30
Q368
95-th percentile164
Maximum581
Range747
Interquartile range (IQR)61

Descriptive statistics

Standard deviation57.93485534
Coefficient of variation (CV)1.257382866
Kurtosis5.190867328
Mean46.07574741
Median Absolute Deviation (MAD)27
Skewness1.781007871
Sum2677047
Variance3356.447464
MonotonicityNot monotonic
2022-05-10T12:42:51.503411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03815
 
6.6%
10931
 
1.6%
3919
 
1.6%
7913
 
1.6%
6840
 
1.4%
4835
 
1.4%
13794
 
1.4%
16776
 
1.3%
5749
 
1.3%
20747
 
1.3%
Other values (534)46782
80.5%
ValueCountFrequency (%)
-1661
< 0.1%
-1571
< 0.1%
-1522
< 0.1%
-1511
< 0.1%
-1451
< 0.1%
-1441
< 0.1%
-1401
< 0.1%
-1351
< 0.1%
-1341
< 0.1%
-1332
< 0.1%
ValueCountFrequency (%)
5811
< 0.1%
5541
< 0.1%
5501
< 0.1%
5411
< 0.1%
5371
< 0.1%
5241
< 0.1%
5122
< 0.1%
5081
< 0.1%
5041
< 0.1%
4951
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5149
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2358.679816
Minimum0
Maximum7117
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:51.670753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile390
Q11114
median2012
Q33330
95-th percentile5490
Maximum7117
Range7117
Interquartile range (IQR)2216

Descriptive statistics

Standard deviation1557.925053
Coefficient of variation (CV)0.6605072221
Kurtosis-0.3780359474
Mean2358.679816
Median Absolute Deviation (MAD)1042
Skewness0.7134778693
Sum137041656
Variance2427130.472
MonotonicityNot monotonic
2022-05-10T12:42:51.788927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150135
 
0.2%
618123
 
0.2%
960107
 
0.2%
1140104
 
0.2%
39095
 
0.2%
99094
 
0.2%
111093
 
0.2%
75092
 
0.2%
105087
 
0.1%
45086
 
0.1%
Other values (5139)57085
98.3%
ValueCountFrequency (%)
015
 
< 0.1%
3031
0.1%
4214
 
< 0.1%
6025
 
< 0.1%
6727
< 0.1%
8539
0.1%
9043
0.1%
9537
0.1%
10853
0.1%
12064
0.1%
ValueCountFrequency (%)
71171
< 0.1%
70441
< 0.1%
70301
< 0.1%
70111
< 0.1%
69661
< 0.1%
69641
< 0.1%
69501
< 0.1%
69431
< 0.1%
69251
< 0.1%
68961
< 0.1%

Hillshade_9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct193
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.2457617
Minimum52
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:51.914348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile161
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range202
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.73122156
Coefficient of variation (CV)0.125944666
Kurtosis1.937160338
Mean212.2457617
Median Absolute Deviation (MAD)16
Skewness-1.193293007
Sum12331691
Variance714.558206
MonotonicityNot monotonic
2022-05-10T12:42:52.042692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2281182
 
2.0%
2261174
 
2.0%
2221130
 
1.9%
2231130
 
1.9%
2301128
 
1.9%
2241103
 
1.9%
2211068
 
1.8%
2321064
 
1.8%
2311046
 
1.8%
2291020
 
1.8%
Other values (183)47056
81.0%
ValueCountFrequency (%)
521
< 0.1%
571
< 0.1%
581
< 0.1%
602
< 0.1%
611
< 0.1%
621
< 0.1%
651
< 0.1%
662
< 0.1%
681
< 0.1%
692
< 0.1%
ValueCountFrequency (%)
254216
 
0.4%
253203
 
0.3%
252271
0.5%
251298
0.5%
250311
0.5%
249388
0.7%
248391
0.7%
247426
0.7%
246505
0.9%
245579
1.0%

Hillshade_Noon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.303024
Minimum71
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:52.167814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range183
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.76839432
Coefficient of variation (CV)0.08852721277
Kurtosis1.855013898
Mean223.303024
Median Absolute Deviation (MAD)12
Skewness-1.044421737
Sum12974129
Variance390.7894141
MonotonicityNot monotonic
2022-05-10T12:42:52.285936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2281408
 
2.4%
2311401
 
2.4%
2291372
 
2.4%
2331366
 
2.4%
2301342
 
2.3%
2341311
 
2.3%
2271300
 
2.2%
2321297
 
2.2%
2261290
 
2.2%
2231279
 
2.2%
Other values (138)44735
77.0%
ValueCountFrequency (%)
711
< 0.1%
951
< 0.1%
961
< 0.1%
971
< 0.1%
982
< 0.1%
991
< 0.1%
1031
< 0.1%
1052
< 0.1%
1071
< 0.1%
1111
< 0.1%
ValueCountFrequency (%)
254606
1.0%
253634
1.1%
252712
1.2%
251761
1.3%
250826
1.4%
249708
1.2%
248821
1.4%
247847
1.5%
246857
1.5%
245841
1.4%

Hillshade_3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct251
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.3823686
Minimum0
Maximum254
Zeros149
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:52.408482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.24577357
Coefficient of variation (CV)0.2686131291
Kurtosis0.4051359607
Mean142.3823686
Median Absolute Deviation (MAD)25
Skewness-0.2738676857
Sum8272558
Variance1462.739196
MonotonicityNot monotonic
2022-05-10T12:42:52.533615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145749
 
1.3%
143722
 
1.2%
138708
 
1.2%
146706
 
1.2%
142692
 
1.2%
139692
 
1.2%
140680
 
1.2%
144666
 
1.1%
136666
 
1.1%
149665
 
1.1%
Other values (241)51155
88.0%
ValueCountFrequency (%)
0149
0.3%
11
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
66
 
< 0.1%
73
 
< 0.1%
93
 
< 0.1%
102
 
< 0.1%
128
 
< 0.1%
ValueCountFrequency (%)
2541
 
< 0.1%
2531
 
< 0.1%
2512
 
< 0.1%
2503
 
< 0.1%
2498
< 0.1%
2486
< 0.1%
2474
 
< 0.1%
2463
 
< 0.1%
24511
< 0.1%
24410
< 0.1%
Distinct4871
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.753034
Minimum0
Maximum7150
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size454.0 KiB
2022-05-10T12:42:52.663703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11026
median1712
Q32563
95-th percentile5007
Maximum7150
Range7150
Interquartile range (IQR)1537

Descriptive statistics

Standard deviation1336.478852
Coefficient of variation (CV)0.6713433706
Kurtosis1.561144483
Mean1990.753034
Median Absolute Deviation (MAD)753
Skewness1.277326351
Sum115664742
Variance1786175.721
MonotonicityNot monotonic
2022-05-10T12:42:52.794933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618133
 
0.2%
541105
 
0.2%
997103
 
0.2%
942102
 
0.2%
700102
 
0.2%
60799
 
0.2%
108294
 
0.2%
40892
 
0.2%
150792
 
0.2%
75292
 
0.2%
Other values (4861)57087
98.3%
ValueCountFrequency (%)
04
 
< 0.1%
3025
< 0.1%
4227
< 0.1%
6032
0.1%
6747
0.1%
8523
< 0.1%
9024
< 0.1%
9548
0.1%
10847
0.1%
12019
 
< 0.1%
ValueCountFrequency (%)
71501
< 0.1%
71411
< 0.1%
71031
< 0.1%
70951
< 0.1%
70521
< 0.1%
70501
< 0.1%
70371
< 0.1%
70291
< 0.1%
70262
< 0.1%
70101
< 0.1%

Wilderness_Area_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
32076 
1.0
26025 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.032076
55.2%
1.026025
44.8%

Length

2022-05-10T12:42:52.908540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:53.000338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.032076
55.2%
1.026025
44.8%

Most occurring characters

ValueCountFrequency (%)
090177
51.7%
.58101
33.3%
126025
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
090177
77.6%
126025
 
22.4%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
090177
51.7%
.58101
33.3%
126025
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
090177
51.7%
.58101
33.3%
126025
 
14.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
55045 
1.0
 
3056

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055045
94.7%
1.03056
 
5.3%

Length

2022-05-10T12:42:53.324156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:53.411856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.055045
94.7%
1.03056
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0113146
64.9%
.58101
33.3%
13056
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113146
97.4%
13056
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113146
64.9%
.58101
33.3%
13056
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113146
64.9%
.58101
33.3%
13056
 
1.8%

Wilderness_Area_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
32808 
1.0
25293 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.032808
56.5%
1.025293
43.5%

Length

2022-05-10T12:42:53.488617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:53.577119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.032808
56.5%
1.025293
43.5%

Most occurring characters

ValueCountFrequency (%)
090909
52.2%
.58101
33.3%
125293
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
090909
78.2%
125293
 
21.8%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
090909
52.2%
.58101
33.3%
125293
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
090909
52.2%
.58101
33.3%
125293
 
14.5%

Wilderness_Area_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
54374 
1.0
 
3727

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054374
93.6%
1.03727
 
6.4%

Length

2022-05-10T12:42:53.653319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:53.740360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.054374
93.6%
1.03727
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0112475
64.5%
.58101
33.3%
13727
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112475
96.8%
13727
 
3.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112475
64.5%
.58101
33.3%
13727
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112475
64.5%
.58101
33.3%
13727
 
2.1%

Soil_Type_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57795 
1.0
 
306

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057795
99.5%
1.0306
 
0.5%

Length

2022-05-10T12:42:53.816856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:53.903180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057795
99.5%
1.0306
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0115896
66.5%
.58101
33.3%
1306
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115896
99.7%
1306
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115896
66.5%
.58101
33.3%
1306
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115896
66.5%
.58101
33.3%
1306
 
0.2%

Soil_Type_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57337 
1.0
 
764

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057337
98.7%
1.0764
 
1.3%

Length

2022-05-10T12:42:53.979547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.068372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057337
98.7%
1.0764
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0115438
66.2%
.58101
33.3%
1764
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115438
99.3%
1764
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115438
66.2%
.58101
33.3%
1764
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115438
66.2%
.58101
33.3%
1764
 
0.4%

Soil_Type_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57633 
1.0
 
468

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057633
99.2%
1.0468
 
0.8%

Length

2022-05-10T12:42:54.145118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.231290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057633
99.2%
1.0468
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0115734
66.4%
.58101
33.3%
1468
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115734
99.6%
1468
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115734
66.4%
.58101
33.3%
1468
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115734
66.4%
.58101
33.3%
1468
 
0.3%

Soil_Type_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
56874 
1.0
 
1227

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056874
97.9%
1.01227
 
2.1%

Length

2022-05-10T12:42:54.307153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.395895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.056874
97.9%
1.01227
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0114975
66.0%
.58101
33.3%
11227
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114975
98.9%
11227
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114975
66.0%
.58101
33.3%
11227
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114975
66.0%
.58101
33.3%
11227
 
0.7%

Soil_Type_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57914 
1.0
 
187

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057914
99.7%
1.0187
 
0.3%

Length

2022-05-10T12:42:54.477255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.564363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057914
99.7%
1.0187
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0116015
66.6%
.58101
33.3%
1187
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116015
99.8%
1187
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116015
66.6%
.58101
33.3%
1187
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116015
66.6%
.58101
33.3%
1187
 
0.1%

Soil_Type_5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57478 
1.0
 
623

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057478
98.9%
1.0623
 
1.1%

Length

2022-05-10T12:42:54.640628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.726948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057478
98.9%
1.0623
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0115579
66.3%
.58101
33.3%
1623
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115579
99.5%
1623
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115579
66.3%
.58101
33.3%
1623
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115579
66.3%
.58101
33.3%
1623
 
0.4%

Soil_Type_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58092 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058092
> 99.9%
1.09
 
< 0.1%

Length

2022-05-10T12:42:54.803499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:54.889988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058092
> 99.9%
1.09
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0116193
66.7%
.58101
33.3%
19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116193
> 99.9%
19
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116193
66.7%
.58101
33.3%
19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116193
66.7%
.58101
33.3%
19
 
< 0.1%

Soil_Type_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58083 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058083
> 99.9%
1.018
 
< 0.1%

Length

2022-05-10T12:42:54.966646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.053200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058083
> 99.9%
1.018
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0116184
66.7%
.58101
33.3%
118
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116184
> 99.9%
118
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116184
66.7%
.58101
33.3%
118
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116184
66.7%
.58101
33.3%
118
 
< 0.1%

Soil_Type_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57997 
1.0
 
104

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057997
99.8%
1.0104
 
0.2%

Length

2022-05-10T12:42:55.129408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.216542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057997
99.8%
1.0104
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0116098
66.6%
.58101
33.3%
1104
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116098
99.9%
1104
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116098
66.6%
.58101
33.3%
1104
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116098
66.6%
.58101
33.3%
1104
 
0.1%

Soil_Type_9
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
54830 
1.0
 
3271

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054830
94.4%
1.03271
 
5.6%

Length

2022-05-10T12:42:55.292867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.379789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.054830
94.4%
1.03271
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0112931
64.8%
.58101
33.3%
13271
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112931
97.2%
13271
 
2.8%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112931
64.8%
.58101
33.3%
13271
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112931
64.8%
.58101
33.3%
13271
 
1.9%

Soil_Type_10
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
56869 
1.0
 
1232

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056869
97.9%
1.01232
 
2.1%

Length

2022-05-10T12:42:55.455963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.542328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.056869
97.9%
1.01232
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0114970
66.0%
.58101
33.3%
11232
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114970
98.9%
11232
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114970
66.0%
.58101
33.3%
11232
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114970
66.0%
.58101
33.3%
11232
 
0.7%

Soil_Type_11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
55035 
1.0
 
3066

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055035
94.7%
1.03066
 
5.3%

Length

2022-05-10T12:42:55.618131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.704684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.055035
94.7%
1.03066
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0113136
64.9%
.58101
33.3%
13066
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113136
97.4%
13066
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113136
64.9%
.58101
33.3%
13066
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113136
64.9%
.58101
33.3%
13066
 
1.8%

Soil_Type_12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
56358 
1.0
 
1743

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056358
97.0%
1.01743
 
3.0%

Length

2022-05-10T12:42:55.781487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:55.867905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.056358
97.0%
1.01743
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0114459
65.7%
.58101
33.3%
11743
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114459
98.5%
11743
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114459
65.7%
.58101
33.3%
11743
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114459
65.7%
.58101
33.3%
11743
 
1.0%

Soil_Type_13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58047 
1.0
 
54

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058047
99.9%
1.054
 
0.1%

Length

2022-05-10T12:42:55.944464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.031333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058047
99.9%
1.054
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0116148
66.6%
.58101
33.3%
154
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116148
> 99.9%
154
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116148
66.6%
.58101
33.3%
154
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116148
66.6%
.58101
33.3%
154
 
< 0.1%

Soil_Type_14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58100 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058100
> 99.9%
1.01
 
< 0.1%

Length

2022-05-10T12:42:56.107243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.195053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058100
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0116201
66.7%
.58101
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116201
> 99.9%
11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116201
66.7%
.58101
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116201
66.7%
.58101
33.3%
11
 
< 0.1%

Soil_Type_15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57789 
1.0
 
312

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057789
99.5%
1.0312
 
0.5%

Length

2022-05-10T12:42:56.272348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.359412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057789
99.5%
1.0312
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0115890
66.5%
.58101
33.3%
1312
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115890
99.7%
1312
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115890
66.5%
.58101
33.3%
1312
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115890
66.5%
.58101
33.3%
1312
 
0.2%

Soil_Type_16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57775 
1.0
 
326

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057775
99.4%
1.0326
 
0.6%

Length

2022-05-10T12:42:56.436320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.522729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057775
99.4%
1.0326
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0115876
66.5%
.58101
33.3%
1326
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115876
99.7%
1326
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115876
66.5%
.58101
33.3%
1326
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115876
66.5%
.58101
33.3%
1326
 
0.2%

Soil_Type_17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57912 
1.0
 
189

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057912
99.7%
1.0189
 
0.3%

Length

2022-05-10T12:42:56.599071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.686173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057912
99.7%
1.0189
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0116013
66.6%
.58101
33.3%
1189
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116013
99.8%
1189
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116013
66.6%
.58101
33.3%
1189
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116013
66.6%
.58101
33.3%
1189
 
0.1%

Soil_Type_18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57686 
1.0
 
415

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057686
99.3%
1.0415
 
0.7%

Length

2022-05-10T12:42:56.762339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:56.848424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057686
99.3%
1.0415
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0115787
66.4%
.58101
33.3%
1415
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115787
99.6%
1415
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115787
66.4%
.58101
33.3%
1415
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115787
66.4%
.58101
33.3%
1415
 
0.2%

Soil_Type_19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57201 
1.0
 
900

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057201
98.5%
1.0900
 
1.5%

Length

2022-05-10T12:42:56.927355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.048508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057201
98.5%
1.0900
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0115302
66.2%
.58101
33.3%
1900
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115302
99.2%
1900
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115302
66.2%
.58101
33.3%
1900
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115302
66.2%
.58101
33.3%
1900
 
0.5%

Soil_Type_20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57991 
1.0
 
110

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057991
99.8%
1.0110
 
0.2%

Length

2022-05-10T12:42:57.124728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.210720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057991
99.8%
1.0110
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0116092
66.6%
.58101
33.3%
1110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116092
99.9%
1110
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116092
66.6%
.58101
33.3%
1110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116092
66.6%
.58101
33.3%
1110
 
0.1%

Soil_Type_21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
54727 
1.0
 
3374

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054727
94.2%
1.03374
 
5.8%

Length

2022-05-10T12:42:57.287146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.373516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.054727
94.2%
1.03374
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0112828
64.7%
.58101
33.3%
13374
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112828
97.1%
13374
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112828
64.7%
.58101
33.3%
13374
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112828
64.7%
.58101
33.3%
13374
 
1.9%

Soil_Type_22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
52443 
1.0
5658 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.052443
90.3%
1.05658
 
9.7%

Length

2022-05-10T12:42:57.449083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.535501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.052443
90.3%
1.05658
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0110544
63.4%
.58101
33.3%
15658
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110544
95.1%
15658
 
4.9%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110544
63.4%
.58101
33.3%
15658
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110544
63.4%
.58101
33.3%
15658
 
3.2%

Soil_Type_23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
55925 
1.0
 
2176

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055925
96.3%
1.02176
 
3.7%

Length

2022-05-10T12:42:57.611767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.698634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.055925
96.3%
1.02176
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0114026
65.4%
.58101
33.3%
12176
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114026
98.1%
12176
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114026
65.4%
.58101
33.3%
12176
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114026
65.4%
.58101
33.3%
12176
 
1.2%

Soil_Type_24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58042 
1.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058042
99.9%
1.059
 
0.1%

Length

2022-05-10T12:42:57.775835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:57.862035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058042
99.9%
1.059
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0116143
66.6%
.58101
33.3%
159
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116143
99.9%
159
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116143
66.6%
.58101
33.3%
159
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116143
66.6%
.58101
33.3%
159
 
< 0.1%

Soil_Type_25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57832 
1.0
 
269

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057832
99.5%
1.0269
 
0.5%

Length

2022-05-10T12:42:57.938197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.025093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057832
99.5%
1.0269
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0115933
66.5%
.58101
33.3%
1269
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115933
99.8%
1269
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115933
66.5%
.58101
33.3%
1269
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115933
66.5%
.58101
33.3%
1269
 
0.2%

Soil_Type_26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57995 
1.0
 
106

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057995
99.8%
1.0106
 
0.2%

Length

2022-05-10T12:42:58.101404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.187722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057995
99.8%
1.0106
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0116096
66.6%
.58101
33.3%
1106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116096
99.9%
1106
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116096
66.6%
.58101
33.3%
1106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116096
66.6%
.58101
33.3%
1106
 
0.1%

Soil_Type_27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58005 
1.0
 
96

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058005
99.8%
1.096
 
0.2%

Length

2022-05-10T12:42:58.264979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.351377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058005
99.8%
1.096
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0116106
66.6%
.58101
33.3%
196
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116106
99.9%
196
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116106
66.6%
.58101
33.3%
196
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116106
66.6%
.58101
33.3%
196
 
0.1%

Soil_Type_28
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
46707 
1.0
11394 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.046707
80.4%
1.011394
 
19.6%

Length

2022-05-10T12:42:58.428327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.515374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.046707
80.4%
1.011394
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0104808
60.1%
.58101
33.3%
111394
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104808
90.2%
111394
 
9.8%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104808
60.1%
.58101
33.3%
111394
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104808
60.1%
.58101
33.3%
111394
 
6.5%

Soil_Type_29
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
55054 
1.0
 
3047

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055054
94.8%
1.03047
 
5.2%

Length

2022-05-10T12:42:58.592260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.677877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.055054
94.8%
1.03047
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0113155
64.9%
.58101
33.3%
13047
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113155
97.4%
13047
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113155
64.9%
.58101
33.3%
13047
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113155
64.9%
.58101
33.3%
13047
 
1.7%

Soil_Type_30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
55534 
1.0
 
2567

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055534
95.6%
1.02567
 
4.4%

Length

2022-05-10T12:42:58.754359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:58.841367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.055534
95.6%
1.02567
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0113635
65.2%
.58101
33.3%
12567
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113635
97.8%
12567
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113635
65.2%
.58101
33.3%
12567
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113635
65.2%
.58101
33.3%
12567
 
1.5%

Soil_Type_31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
52787 
1.0
5314 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.052787
90.9%
1.05314
 
9.1%

Length

2022-05-10T12:42:58.918019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:59.004117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.052787
90.9%
1.05314
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0110888
63.6%
.58101
33.3%
15314
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110888
95.4%
15314
 
4.6%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110888
63.6%
.58101
33.3%
15314
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110888
63.6%
.58101
33.3%
15314
 
3.0%

Soil_Type_32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
53573 
1.0
 
4528

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.053573
92.2%
1.04528
 
7.8%

Length

2022-05-10T12:42:59.418000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:59.510644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.053573
92.2%
1.04528
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0111674
64.1%
.58101
33.3%
14528
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0111674
96.1%
14528
 
3.9%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0111674
64.1%
.58101
33.3%
14528
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0111674
64.1%
.58101
33.3%
14528
 
2.6%

Soil_Type_33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57955 
1.0
 
146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057955
99.7%
1.0146
 
0.3%

Length

2022-05-10T12:42:59.586669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:59.672772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057955
99.7%
1.0146
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0116056
66.6%
.58101
33.3%
1146
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116056
99.9%
1146
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116056
66.6%
.58101
33.3%
1146
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116056
66.6%
.58101
33.3%
1146
 
0.1%

Soil_Type_34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57927 
1.0
 
174

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057927
99.7%
1.0174
 
0.3%

Length

2022-05-10T12:42:59.749015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:59.835332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057927
99.7%
1.0174
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0116028
66.6%
.58101
33.3%
1174
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116028
99.9%
1174
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116028
66.6%
.58101
33.3%
1174
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116028
66.6%
.58101
33.3%
1174
 
0.1%

Soil_Type_35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58087 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058087
> 99.9%
1.014
 
< 0.1%

Length

2022-05-10T12:42:59.911108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:42:59.996936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058087
> 99.9%
1.014
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0116188
66.7%
.58101
33.3%
114
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116188
> 99.9%
114
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116188
66.7%
.58101
33.3%
114
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116188
66.7%
.58101
33.3%
114
 
< 0.1%

Soil_Type_36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
58066 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058066
99.9%
1.035
 
0.1%

Length

2022-05-10T12:43:00.077669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:43:00.163709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.058066
99.9%
1.035
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0116167
66.6%
.58101
33.3%
135
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116167
> 99.9%
135
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116167
66.6%
.58101
33.3%
135
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116167
66.6%
.58101
33.3%
135
 
< 0.1%

Soil_Type_37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
56537 
1.0
 
1564

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056537
97.3%
1.01564
 
2.7%

Length

2022-05-10T12:43:00.239688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:43:00.326032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.056537
97.3%
1.01564
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0114638
65.8%
.58101
33.3%
11564
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114638
98.7%
11564
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114638
65.8%
.58101
33.3%
11564
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114638
65.8%
.58101
33.3%
11564
 
0.9%

Soil_Type_38
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
56684 
1.0
 
1417

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056684
97.6%
1.01417
 
2.4%

Length

2022-05-10T12:43:00.402492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:43:00.488677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.056684
97.6%
1.01417
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0114785
65.9%
.58101
33.3%
11417
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0114785
98.8%
11417
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0114785
65.9%
.58101
33.3%
11417
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0114785
65.9%
.58101
33.3%
11417
 
0.8%

Soil_Type_39
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size454.0 KiB
0.0
57263 
1.0
 
838

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174303
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057263
98.6%
1.0838
 
1.4%

Length

2022-05-10T12:43:00.565237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T12:43:00.652074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.057263
98.6%
1.0838
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0115364
66.2%
.58101
33.3%
1838
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number116202
66.7%
Other Punctuation58101
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0115364
99.3%
1838
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.58101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common174303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0115364
66.2%
.58101
33.3%
1838
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII174303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0115364
66.2%
.58101
33.3%
1838
 
0.5%

Cover_Type
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
False
54560 
True
 
3541
ValueCountFrequency (%)
False54560
93.9%
True3541
 
6.1%
2022-05-10T12:43:00.726328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2022-05-10T12:42:46.598041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:33.896687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.315510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.720551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.350651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.756840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.171928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.636268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.995096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.317505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.735583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.099551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.456501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.903857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.481436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.896203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.302839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.767998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.126473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.444424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.901108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.232663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.599035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.040435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.610224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.038879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.434365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.899837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.254840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.564952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.038400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.364474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.743446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.164953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.755384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.195347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.565679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.032164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.392164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.689446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.172872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.499420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.878161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.317996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.904554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.340033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.696416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.183982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.517505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.823610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.311369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.650713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.018464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.618600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.050035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.487999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.833339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.322048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.654020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.952373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.453754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.783586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.163563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.762808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.194276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.623471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.965802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.459799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.790579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.092449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.599268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:34.922340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.306387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:37.901065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.340822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.763188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.100085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.594806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:44.921192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.218389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.738978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.054214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.444626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.043187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.484041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:40.905049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.231762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.724360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.055292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.340176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:47.871431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:35.177303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:36.568815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:38.197779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:39.611442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:41.038115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:42.494767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:43.856046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:45.186022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-10T12:42:46.462570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-10T12:43:00.885228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-10T12:43:02.194676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-10T12:43:03.341958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-10T12:43:04.454071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-10T12:43:04.887880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-10T12:42:48.800922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
03021.026.016.060.07.03961.0211.0204.0125.02496.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
13065.0348.021.0124.019.04725.0177.0202.0159.0624.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False
23153.0287.017.0335.041.01298.0171.0237.0205.02045.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0False
33172.0156.029.0716.0291.01154.0237.0228.098.02837.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False
43182.070.013.0362.040.02992.0234.0214.0109.04336.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
52712.044.014.0511.032.01282.0222.0208.0117.02759.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
62991.016.012.0210.035.04049.0210.0215.0141.01040.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False
73099.0240.016.0228.057.02989.0187.0252.0200.01485.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
82633.0108.014.0182.021.0764.0243.0223.0106.01146.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
92771.057.025.0323.0108.0342.0228.0178.069.02188.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False

Last rows

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
580912342.0359.021.0390.0217.0969.0184.0197.0146.01328.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True
580923276.087.015.0124.06.03394.0241.0215.0100.01302.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
580933210.0302.09.0433.0-10.05419.0195.0236.0181.01871.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
580942693.059.010.030.07.03234.0228.0219.0124.06097.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.0False
580953358.0205.015.0532.0201.02438.0210.0252.0174.0914.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0False
580962937.02.016.0170.028.02538.0196.0208.0148.01943.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0False
580972841.085.022.0162.019.0295.0245.0197.071.0201.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False
580983034.0102.07.0268.0133.02522.0232.0231.0132.01766.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0False
580993272.0329.011.060.010.04650.0194.0227.0173.0836.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0False
581003228.0203.022.0228.075.01614.0204.0253.0175.04234.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False

Duplicate rows

Most frequently occurring

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type# duplicates
9552906.0181.024.0182.055.04043.0220.0247.0146.01451.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.0False5
11062945.0328.08.042.02.03903.0202.0231.0169.01206.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False4
432238.083.031.067.034.01351.0245.0172.033.0680.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
442238.0321.031.0300.0101.01026.0128.0194.0198.0872.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0False3
452239.0228.020.030.09.01113.0186.0254.0199.0524.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
512277.0104.035.0162.077.01652.0253.0173.00.0108.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
602296.0321.023.0150.069.0968.0156.0212.0192.0242.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
782350.0110.024.0242.076.0228.0252.0206.068.01095.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
1192430.0117.028.0234.089.0342.0254.0202.056.0854.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0True3
2022549.049.020.0376.030.0330.0224.0194.096.02509.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0False3